A Continuous-Time Consistency Model for 3D Point Cloud Generation

2025-09-01

AI Summary

This work addresses key limitations in point-cloud-based 3D shape generation—namely, reliance on discrete diffusion steps, pre-trained teacher models, or latent-space encoding. We propose ConTiCoM-3D, the first continuous-time consistency model operating directly in the raw point-cloud space. Our method eliminates discretization and external supervision by leveraging a time-conditioned neural network, a TrigFlow-inspired continuous noise schedule, and a Chamfer-distance-driven geometric loss, enabling stable training and end-to-end optimization over high-dimensional point sets. Crucially, it avoids Jacobian-vector product computations and operates entirely in the native point space, supporting efficient one- or two-step inference. Evaluated on ShapeNet, ConTiCoM-3D matches or surpasses state-of-the-art diffusion and latent-space consistency models in both generation quality and inference speed. This demonstrates the effectiveness and practicality of continuous-time modeling for scalable 3D generative learning.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Fast and accurate 3D shape generation from point clouds is essential for applications in robotics, AR/VR, and digital content creation. We introduce ConTiCoM-3D, a continuous-time consistency model that synthesizes 3D shapes directly in point space, without discretized diffusion steps, pre-trained teacher models, or latent-space encodings. The method integrates a TrigFlow-inspired continuous noise schedule with a Chamfer Distance-based geometric loss, enabling stable training on high-dimensional point sets while avoiding expensive Jacobian-vector products. This design supports efficient one- to two-step inference with high geometric fidelity. In contrast to previous approaches that rely on iterative denoising or latent decoders, ConTiCoM-3D employs a time-conditioned neural network operating entirely in continuous time, thereby achieving fast generation. Experiments on the ShapeNet benchmark show that ConTiCoM-3D matches or outperforms state-of-the-art diffusion and latent consistency models in both quality and efficiency, establishing it as a practical framework for scalable 3D shape generation.

Problem

• Generating 3D shapes from point clouds efficiently
• Avoiding discretized diffusion steps and latent encodings
• Achieving high geometric fidelity with fast inference

Innovation

• Continuous-time consistency model without diffusion steps
• Integrates continuous noise schedule with geometric loss
• Employs time-conditioned neural network in continuous time